Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [3]:
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()

fig = px.bar(df_2007_new, y="continent", x="pop", color="continent", orientation="h", hover_name = 'continent', text = 'pop',
             color_discrete_map={
                "Europe": "red",
                "Asia": "green",
                "Americas": "blue",
                "Oceania": "goldenrod",
                "Africa": "magenta"},
             title="Question 1"
            )
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [4]:
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()

fig = px.bar(df_2007_new, y="continent", x="pop", color="continent", orientation="h", hover_name = 'continent', text = 'pop',
             color_discrete_map={
                "Europe": "red",
                "Asia": "green",
                "Americas": "blue",
                "Oceania": "goldenrod",
                "Africa": "magenta"},
             title="Question 2"
            )
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 3:¶

Add text to each bar that represents the population

In [5]:
# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()

fig = px.bar(df_2007_new, y="continent", x="pop", color="continent", orientation="h", hover_name = 'continent', text = 'pop',
             color_discrete_map={
                "Europe": "red",
                "Asia": "green",
                "Americas": "blue",
                "Oceania": "goldenrod",
                "Africa": "magenta"},
             title="Question 3"
            )
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [6]:
# YOUR CODE HERE
df_grouped = df.groupby(['continent', 'year']).sum()
df_grouped = df_grouped.reset_index()


fig = px.bar(df_grouped, y="continent", x="pop", color="continent", orientation="h", hover_name = 'continent', 
             text = 'pop', animation_frame="year",
             color_discrete_map={
                "Europe": "red",
                "Asia": "green",
                "Americas": "blue",
                "Oceania": "goldenrod",
                "Africa": "magenta"},
             title="Question 4"
            )

fig.update_xaxes(range=[0, 4000000000])
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [7]:
# YOUR CODE HERE
df_grouped = df.groupby(['country', 'year']).sum()
df_grouped = df_grouped.reset_index()


fig = px.bar(df_grouped, y="country", x="pop", color="country", orientation="h", hover_name = 'country', 
             text = 'pop', animation_frame="year",
             title="Question 5",
            )

fig.update_xaxes(range=[0, 1500000000])
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [8]:
# YOUR CODE HERE
df_grouped = df.groupby(['country', 'year']).sum()
df_grouped = df_grouped.reset_index()


fig = px.bar(df_grouped, y="country", x="pop", color="country", orientation="h", hover_name = 'country', 
             text = 'pop', animation_frame="year",
             title="Question 6",
             height=1000
            )

fig.update_xaxes(range=[0, 1500000000])
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [9]:
# YOUR CODE HERE
df_grouped = df.groupby(['country', 'year']).sum()
df_grouped = df_grouped.reset_index()


fig = px.bar(df_grouped, y="country", x="pop", color="country", orientation="h", hover_name = 'country', 
             text = 'pop', animation_frame="year",
             title="Question 7",
             height = 1000
            )


x = len(df_grouped)

fig.update_xaxes(range=[0, 1500000000])
fig.update_yaxes(range=(9.5, -0.5))
fig.update_yaxes(categoryorder="total descending")

fig.show()